Effective management of dense-traffic queues and waiting lines is a critical task in many environments, such as government buildings, heavy traffic roads, theme parks, sport stadiums, retail stores, etc. For example, in a supermarket, a manager can optimize usage of a check-out lane based on the current waiting line status. In another example, the queue density and speed information of vehicles on a highway can be used to provide accurate traffic reports.
Due to the nature of high-density queues, it is extremely difficult to reliably segment and track “blobs”, which correspond to semantically meaningful objects in the scene. Thus, traditional blob-based object tracking algorithms are not capable of managing high-density traffic queues.
Given the difficulties of extracting meaningful and reliable global information of the blobs, many approaches have turned their focus to the analysis of local information of the dense flow and developed trackers based on image points. Many of the point trackers assume consistency in the neighborhood of the points being tracked, such as scale invariance or Affine invariance. The image points are tracked to form trajectories, which can be used in further scene or activity analysis. The main drawback of using point-trackers in dense-traffic queue analysis is that they only capture the local properties of the queue and do not have the ability to represent the objects in the scene. Therefore, point-trackers are not able to provide more global descriptions of the queue, such as the number of objects in the queue or the density of the queue.
Accordingly, there is a need for a solution that addresses these and other deficiencies of the related art.